Zero-shot denoisers are flexible and easy to use. Since they train from scratch on each image, \(D\)  is only limited by the set of images the neural network actually works on, rather than being constrained by the contents of some training set. If the method is also blind, in that it doesn't require a user supplied estimate of the noise distribution, this set grows even larger. The most accurate blind zero-shot denoisers are blind-spot networks, particularly S2S [1]}.
